Vision-based inspection and segmentation of trimmed steel edges

Zeiler, Alexander; Steinboeck, Andreas; Vincze, Markus; Jochum, Martin; Kugi, Andreas

doi:10.1016/j.ifacol.2019.09.182

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…The mathematical morphology algorithm with multi-structural elements can not only extract small edges, but also suppress noise. Morphology used to image segmentation for steel surface defects has many instances, such as [38,55,[85][86][87][88]. Tang et al [89] studied the edge detection of strip surface defect by using the mathematical morphology of multi-structural elements, and effectively detected the edge of weak and small targets.…”

Section: 35mentioning

confidence: 99%

Review of surface defect detection of steel products based on machine vision

Tang

Chen

Sun

et al. 2022

IET Image Processing

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Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state‐of‐art of vision‐based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision‐based surface defect inspection for steel products including hardware system, automated vision‐based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre‐processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected.

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Section: 35mentioning

confidence: 99%

Review of surface defect detection of steel products based on machine vision

Tang

Chen

Sun

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

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“…At the electronics company, machine vision was developed to increase automation and speed up the change-over product process [14]. In the steel plate industry, the automatic vision system is used to detect the quality of the cut steel plate by knowing the fracture area that is damaged and burned [15]. The combination of an electromechanical system is the basis for developing a fully automatic contactless inspection for testing bearings with the 608ZZ type [16].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of intelligent electronic component inspection based on PLC and vision system

Ikatrinasari,

Herlambang,

Kosasih

2023

j. sist. manaj. ind.

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Customer demands for product quality are increasingly complex, requiring better inspection accuracy. It is not enough if done manually because it requires high costs and varying operator accuracy. Automatic vision inspection was developed to check the product quality of terminal-type electronic components To solve this problem. Design intelligent inspection uses a conveyor driven by a stepper motor, a photosensor to calculate product distance, guides position to direct the product, a vision camera to detect product quality, cylinder ejection for product selection, and PLC as a control system. The process of detecting normal and abnormal product quality is carried out using computer logic control, then separating the abnormal product into the reject box through the ejection cylinder. The machine speed is 60 pieces/minute. The system evaluation results are carried out on three parts of the system: the success rate on the vision camera is 100%, automatic product sorting through the cylinder ejection rate success is 100%, and the success rate for product positioning is 97.5%. This research provides a useful reference for developing intelligent automatic inspection technology in electronic components.

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“…In electronic companies, machine vision has been developed to improve the degree of automation and accelerate product transformation [20]. In the steel plate industry, machine vision systems are used to detect the quality of cut steel plates by identifying damaged and burned fault zones [21]. The combination of electromechanical systems became the basis for the development of a fully automated non-contact testing method for 608ZZ type bearing testing [22].…”

Section: Introductionmentioning

confidence: 99%

Terminals and Connector Inspection Innovation using Machine Vision

Herlambang,

Semnasti,

Semnasti

2023

waluyojatmiko

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Customer demands for product quality that are increasingly complex and require more accurate inspection are not sufficient if done manually due to high costs and different operator precision. To solve this problem, automatic vision inspection was created to check the product quality of terminal-type electronic components. Machine Vision is robust equipment for examining electrical components, agroindustry, surface mounting technology industry, plastic industry, and glass industry. In this research, the design and application of machine vision are used to inspect terminals and connectors, supporting automotive and electrical equipment components. Machine vision checks three quality functions, namely structural quality, surface quality, and dimensional quality. Optimization of image capture using backlighting and darkfield lighting. Implementing machine vision is carried out by verifying image retrieval's stability using a gage study with the results that there is no measurement bias.

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Vision-based inspection and segmentation of trimmed steel edges

Cited by 7 publications

References 5 publications

Review of surface defect detection of steel products based on machine vision

Review of surface defect detection of steel products based on machine vision

Design and implementation of intelligent electronic component inspection based on PLC and vision system

Terminals and Connector Inspection Innovation using Machine Vision

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